{
  "meta": {
    "pretty_name": "TIR-Bench",
    "dataset_id": "evalscope/TIR-Bench",
    "paper_url": "https://arxiv.org/abs/2511.01833",
    "tags": [
      "MultiModal",
      "Reasoning",
      "QA"
    ],
    "metrics": [
      "acc"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "instrument",
      "color",
      "refcoco",
      "rotation_game",
      "math",
      "word_search",
      "visual_search",
      "ocr",
      "symbolic",
      "spot_difference",
      "contrast",
      "jigsaw",
      "maze"
    ],
    "description": "\n## Overview\n\nTIR-Bench (Thinking-with-Images Reasoning Benchmark) is a comprehensive multimodal benchmark\nthat evaluates agentic visual reasoning capabilities of vision-language models. It covers\ndiverse task categories requiring spatial, compositional, and multi-step visual reasoning.\n\n## Task Description\n\n- **Task Type**: Multi-task Visual Reasoning (MCQ, OCR, word search, spot difference, jigsaw, etc.)\n- **Input**: One or two images + question (most tasks use multiple-choice format)\n- **Output**: Answer letter (MCQ) or numeric/text response depending on task type\n- **Domains**: instrument, color, refcoco, rotation_game, math, word_search, visual_search, ocr, symbolic, spot_difference, contrast, jigsaw, maze\n\n## Key Features\n\n- 1,215 test samples across 13 diverse visual reasoning task categories\n- Covers single-image and dual-image reasoning scenarios\n- Answers span letter choices (A-J), integers, floats, and text\n- Task-specific scoring with LLM-as-judge fallback for robust evaluation\n\n## Evaluation Notes\n\n- Default evaluation uses the **test** split (1,215 samples)\n- Primary metric: **Accuracy** (acc)\n- Images are downloaded as `data.zip` from ModelScope and extracted automatically\n- Rule-based scoring: OCR (substring match), jigsaw (grid IoU), spot_difference (set IoU),\n  word_search (numeric match), all other tasks (MCQ / numeric judge)\n- **Recommended**: set `judge_strategy=JudgeStrategy.LLM_RECALL` and provide `judge_model_args`\n  to activate LLM-as-judge as a recall mechanism — the judge is called only when rule-based\n  scoring gives 0, providing more accurate evaluation without unnecessary API overhead\n- [Paper](https://arxiv.org/abs/2511.01833) | [GitHub](https://github.com/agents-x-project/TIR-Bench)\n",
    "prompt_template": "",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
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    "computed_at": "2026-04-15T17:58:59.028312",
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    "data": {
      "input": [
        {
          "id": "dd25f10d",
          "content": [
            {
              "image": "[BASE64_IMAGE: jpg, ~2.9MB]"
            },
            {
              "text": "According to the image, what is the thermometer reading in Fahrenheit? Answer as an integer like 1,2,3."
            }
          ]
        }
      ],
      "target": "72",
      "id": 0,
      "group_id": 0,
      "subset_key": "instrument",
      "metadata": {
        "task": "instrument",
        "meta_data": {},
        "id": 6
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  "readme": {
    "en": "# TIR-Bench\n\n\n## Overview\n\nTIR-Bench (Thinking-with-Images Reasoning Benchmark) is a comprehensive multimodal benchmark\nthat evaluates agentic visual reasoning capabilities of vision-language models. It covers\ndiverse task categories requiring spatial, compositional, and multi-step visual reasoning.\n\n## Task Description\n\n- **Task Type**: Multi-task Visual Reasoning (MCQ, OCR, word search, spot difference, jigsaw, etc.)\n- **Input**: One or two images + question (most tasks use multiple-choice format)\n- **Output**: Answer letter (MCQ) or numeric/text response depending on task type\n- **Domains**: instrument, color, refcoco, rotation_game, math, word_search, visual_search, ocr, symbolic, spot_difference, contrast, jigsaw, maze\n\n## Key Features\n\n- 1,215 test samples across 13 diverse visual reasoning task categories\n- Covers single-image and dual-image reasoning scenarios\n- Answers span letter choices (A-J), integers, floats, and text\n- Task-specific scoring with LLM-as-judge fallback for robust evaluation\n\n## Evaluation Notes\n\n- Default evaluation uses the **test** split (1,215 samples)\n- Primary metric: **Accuracy** (acc)\n- Images are downloaded as `data.zip` from ModelScope and extracted automatically\n- Rule-based scoring: OCR (substring match), jigsaw (grid IoU), spot_difference (set IoU),\n  word_search (numeric match), all other tasks (MCQ / numeric judge)\n- **Recommended**: set `judge_strategy=JudgeStrategy.LLM_RECALL` and provide `judge_model_args`\n  to activate LLM-as-judge as a recall mechanism — the judge is called only when rule-based\n  scoring gives 0, providing more accurate evaluation without unnecessary API overhead\n- [Paper](https://arxiv.org/abs/2511.01833) | [GitHub](https://github.com/agents-x-project/TIR-Bench)\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `tir_bench` |\n| **Dataset ID** | [evalscope/TIR-Bench](https://modelscope.cn/datasets/evalscope/TIR-Bench/summary) |\n| **Paper** | [Paper](https://arxiv.org/abs/2511.01833) |\n| **Tags** | `MultiModal`, `QA`, `Reasoning` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 1,215 |\n| Prompt Length (Mean) | 384.97 chars |\n| Prompt Length (Min/Max) | 19 / 4039 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `instrument` | 80 | 110.96 | 57 | 196 |\n| `color` | 100 | 130.26 | 98 | 241 |\n| `refcoco` | 120 | 144.51 | 132 | 182 |\n| `rotation_game` | 75 | 146.44 | 140 | 148 |\n| `math` | 120 | 126.69 | 50 | 397 |\n| `word_search` | 100 | 126.72 | 24 | 307 |\n| `visual_search` | 120 | 111.64 | 19 | 501 |\n| `ocr` | 60 | 35.08 | 29 | 116 |\n| `symbolic` | 50 | 88.18 | 66 | 243 |\n| `spot_difference` | 100 | 1114.79 | 93 | 1379 |\n| `contrast` | 50 | 48.1 | 31 | 123 |\n| `jigsaw` | 120 | 605 | 605 | 605 |\n| `maze` | 120 | 1527.06 | 626 | 4039 |\n\n**Image Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Images | 1,255 |\n| Images per Sample | min: 1, max: 2, mean: 1.03 |\n| Resolution Range | 60x23 - 6944x9280 |\n| Formats | jpeg, mpo, png, webp |\n\n\n## Sample Example\n\n**Subset**: `instrument`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"dd25f10d\",\n      \"content\": [\n        {\n          \"image\": \"[BASE64_IMAGE: jpg, ~2.9MB]\"\n        },\n        {\n          \"text\": \"According to the image, what is the thermometer reading in Fahrenheit? Answer as an integer like 1,2,3.\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"72\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"instrument\",\n  \"metadata\": {\n    \"task\": \"instrument\",\n    \"meta_data\": {},\n    \"id\": 6\n  }\n}\n```\n\n## Prompt Template\n\n*No prompt template defined.*\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets tir_bench \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['tir_bench'],\n    dataset_args={\n        'tir_bench': {\n            # subset_list: ['instrument', 'color', 'refcoco']  # optional, evaluate specific subsets\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# TIR-Bench\n\n\n## 概述\n\nTIR-Bench（Thinking-with-Images Reasoning Benchmark）是一个全面的多模态基准测试，用于评估视觉语言模型的具身视觉推理能力。该基准涵盖多种任务类别，要求模型具备空间、组合式及多步骤的视觉推理能力。\n\n## 任务描述\n\n- **任务类型**：多任务视觉推理（选择题、OCR、单词搜索、找不同、拼图等）\n- **输入**：一张或两张图像 + 问题（大多数任务采用选择题格式）\n- **输出**：答案字母（选择题）或数字/文本形式的答案（取决于任务类型）\n- **领域**：instrument（仪器）、color（颜色）、refcoco、rotation_game（旋转游戏）、math（数学）、word_search（单词搜索）、visual_search（视觉搜索）、ocr、symbolic（符号）、spot_difference（找不同）、contrast（对比）、jigsaw（拼图）、maze（迷宫）\n\n## 核心特性\n\n- 包含13种多样化的视觉推理任务类别，共计1,215个测试样本\n- 覆盖单图和双图推理场景\n- 答案形式包括字母选项（A-J）、整数、浮点数和文本\n- 采用任务特定评分机制，并辅以LLM-as-judge作为后备方案，确保评估的鲁棒性\n\n## 评估说明\n\n- 默认使用 **test** 划分进行评估（共1,215个样本）\n- 主要指标：**准确率**（acc）\n- 图像数据通过 ModelScope 下载为 `data.zip` 并自动解压\n- 基于规则的评分方式：\n  - OCR（子串匹配）\n  - 拼图（grid IoU）\n  - 找不同（set IoU）\n  - 单词搜索（数值匹配）\n  - 其他所有任务（选择题 / 数值判断）\n- **推荐设置**：将 `judge_strategy=JudgeStrategy.LLM_RECALL` 并提供 `judge_model_args`，以启用 LLM-as-judge 作为召回机制——仅当基于规则的评分结果为0时才调用判别模型，在避免不必要 API 开销的同时提升评估准确性\n- [论文](https://arxiv.org/abs/2511.01833) | [GitHub](https://github.com/agents-x-project/TIR-Bench)\n\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `tir_bench` |\n| **数据集ID** | [evalscope/TIR-Bench](https://modelscope.cn/datasets/evalscope/TIR-Bench/summary) |\n| **论文** | [Paper](https://arxiv.org/abs/2511.01833) |\n| **标签** | `MultiModal`, `QA`, `Reasoning` |\n| **指标** | `acc` |\n| **默认示例数** | 0-shot |\n| **评估划分** | `test` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 1,215 |\n| 提示词长度（平均） | 384.97 字符 |\n| 提示词长度（最小/最大） | 19 / 4039 字符 |\n\n**各子集统计数据：**\n\n| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |\n|--------|---------|-------------|------------|------------|\n| `instrument` | 80 | 110.96 | 57 | 196 |\n| `color` | 100 | 130.26 | 98 | 241 |\n| `refcoco` | 120 | 144.51 | 132 | 182 |\n| `rotation_game` | 75 | 146.44 | 140 | 148 |\n| `math` | 120 | 126.69 | 50 | 397 |\n| `word_search` | 100 | 126.72 | 24 | 307 |\n| `visual_search` | 120 | 111.64 | 19 | 501 |\n| `ocr` | 60 | 35.08 | 29 | 116 |\n| `symbolic` | 50 | 88.18 | 66 | 243 |\n| `spot_difference` | 100 | 1114.79 | 93 | 1379 |\n| `contrast` | 50 | 48.1 | 31 | 123 |\n| `jigsaw` | 120 | 605 | 605 | 605 |\n| `maze` | 120 | 1527.06 | 626 | 4039 |\n\n**图像统计数据：**\n\n| 指标 | 值 |\n|--------|-------|\n| 图像总数 | 1,255 |\n| 每样本图像数 | 最小: 1, 最大: 2, 平均: 1.03 |\n| 分辨率范围 | 60x23 - 6944x9280 |\n| 格式 | jpeg, mpo, png, webp |\n\n\n## 样例示例\n\n**子集**: `instrument`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"dd25f10d\",\n      \"content\": [\n        {\n          \"image\": \"[BASE64_IMAGE: jpg, ~2.9MB]\"\n        },\n        {\n          \"text\": \"According to the image, what is the thermometer reading in Fahrenheit? Answer as an integer like 1,2,3.\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"72\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"instrument\",\n  \"metadata\": {\n    \"task\": \"instrument\",\n    \"meta_data\": {},\n    \"id\": 6\n  }\n}\n```\n\n## 提示模板\n\n*未定义提示模板。*\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets tir_bench \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['tir_bench'],\n    dataset_args={\n        'tir_bench': {\n            # subset_list: ['instrument', 'color', 'refcoco']  # 可选，用于评估特定子集\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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